class: center, middle, inverse, title-slide .title[ # Creating functions, a first step into R programming ] .subtitle[ ## Illustration with {unhcrplot} ] .date[ ### <a href="index.html"><i class="fa fa-indent fa-fw fa-2x"></i></a> ] --- # Learning objectives * Understand Why Functions are needed * Learn to transition from Scripts to Functions * Call Custom Function * Function Documentation * Plotting Functions * Function Parameter --- ## Why Functions? Code Reusability * Functions allow you to reuse code. * Write a function once and use it multiple times. Modularity * Functions help in breaking down complex tasks into smaller, manageable pieces. * Easier to debug and maintain. Collaboration * Functions make it easier to collaborate with others. * Share functions instead of long scripts. --- ## Transition from Scripts to Functions Script Example ```r # Script to calculate the mean of a numeric vector data <- c(1, 2, 3, 4, 5) mean_value <- mean(data) print(mean_value) ``` ``` ## [1] 3 ``` Function Example ```r # Function to calculate the mean of a numeric vector calculate_mean <- function(data) { mean_value <- mean(data) return(mean_value) } data <- c(1, 2, 3, 4, 5) mean_value <- calculate_mean(data) print(mean_value) ``` ``` ## [1] 3 ``` --- ## Key Differences * Functions encapsulate code into reusable units. * Parameters allow customization of function behavior. Creating Functions in R Function Structure ```r function_name <- function(parameter1, parameter2, ...) { # Function body # Perform operations return(result) } ``` --- ## Example: Custom Function ```r # Custom function to calculate the median of a numeric vector calculate_median <- function(data) { median_value <- median(data) return(median_value) } ``` --- ## Calling Custom Function ```r data <- c(1, 2, 3, 4, 5) median_value <- calculate_median(data) print(median_value) ``` ``` ## [1] 3 ``` --- ## Import Data - Data import: [`readr`](https://readr.tidyverse.org/), [`readxl`](https://readxl.tidyverse.org/), etc. --- ## Start with Documentation Why Document Functions? * Documentation provides information on how to use a function. * Helps other users (including your future self) understand the purpose and usage of the function. * Standard practice for sharing code. R Documentation * Use roxygen2 or devtools for generating R documentation. * Add details about parameters, return values, and examples. Code Style and Consistency * Follow modern and consistent code formatting. * Use tools like styler for code formatting. --- ## Example Documentation ```r #' Calculate the median of a numeric vector. #' #' This function takes a numeric vector and returns its median value. #' #' @param data A numeric vector. #' #' @return The median of the input vector. #' #' @examples #' data <- c(1, 2, 3, 4, 5) #' median_value <- calculate_median(data) #' #' @export calculate_median <- function(data) { median_value <- median(data) return(median_value) } ``` --- ## Use Modular Code * Modular code is organized into small, reusable components. * Makes your code more readable and maintainable. * Reduces redundancy and duplication. Recurrent Plots * Recurrent plots are plots used multiple times in your code or projects. --- ## What is a Plotting Function? * A plotting function is a custom R function that generates a specific plot. * Encapsulates the plot's code and logic. Advantages * Reusability * Consistency * Code organization ```r # Define a plotting function # plot_histogram <- function(data, variable) { # ggplot(data, aes(x = {{ variable }})) + # geom_histogram(binwidth = 1, fill = "lightblue") + # labs(title = paste("Histogram of", deparse(substitute(variable)))) # } ``` --- ## Function Parameters * Plotting functions can accept parameters to customize the plot. * Parameters allow you to create dynamic and flexible plots. Customization Example ```r # Customize the plotting function #plot_histogram(mtcars, mpg) ``` Plot Customization * Allow users to customize plots by providing parameters. * Parameters can control titles, colors, labels, etc. Example with Custom Parameters ```r # Customize plot appearance # plot_histogram(mtcars, mpg, title = "Custom Histogram", fill_color = "lightgreen") ``` --- ## Organizing Plotting Functions * Create a directory or package to store your plotting functions. * Name files descriptively and include documentation. --- ## Advanced Features * Saving plots to svg or png * Creating dynamic dashboards * Integrate your plotting functions into Shiny apps for interactive visualization. --- ## Conclusion: Key Takeaways * Functions are essential for code reusability and modularity. * Transition from scripts to functions for better code organization. * Documenting functions is crucial for effective collaboration and code sharing. * Modular code with plotting functions improves code organization and reusability. * Function parameters allow customization of plots. * Building a directory of plotting functions streamlines your work. --- class: inverse, center, middle # Thank you ### Questions? [post Feedback here](https://github.com/unhcRverse/unhcrverse/issues/new?assignees=&labels=enhancement&projects=&template=comment_prex_2_tidyverse.md&title=%5Blearn%5D) Reference: R programming: [R for Data Science](https://r4ds.had.co.nz/), [Advanced R Programming](https://adv-r.hadley.nz/), etc.